摘要： 在制药领域,人工检测药品速度慢、效率低,而图像处理的方法需要大量样本且适应性差。针对以上问题,提出特征提取与机器学习相结合的方法,介绍用于目标区域特征提取的词袋特征(BOF)算法,分析BOF算法的最佳参数取值,并采用支持向量机对药品分层缺陷进行分类检测。实验结果表明,该方法能使药品达到较高的分类精度,并且满足工业生产实时检测的要求。

关键词: 药品分层, 缺陷检测, 特征提取, 机器学习, 词袋模型, 支持向量机

Abstract: During the pharmaceutical process,manual inspection suffers from time consuming and inefficiency.Image processing method needs plenty of samples and has poor adaptability.To address these problems,a method based on feature extraction and machine learning is developed.The Bag of Feature(BOF) algorithm is introduced to extract the features of the target region and its optimum parameter value is analyzed.Support Vector Machine(SVM) is applied to inspect the defects of the hierarchical drug.Experimental results show that the proposed method can achieve higher classification accuracy and meet the requirements of real-time inspection in the process of industrial production.

Key words: hierarchical drug, defect inspection, feature extraction, machine learning, Bag of Word(BOW)model, Support Vector Machine(SVM)

中图分类号: 

• TP301.6